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Neural Comput. 2017 Sep;29(9):2352-2449. doi: 10.1162/NECO_a_00990. Epub 2017 Jun 9.

Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review.

Author information

1
Department of Electrical and Mining Engineering, University of South Africa, Florida 1710, South Africa wrawat10@gmail.com.
2
Department of Electrical and Mining Engineering, University of South Africa, Florida 1710, South Africa wangz@unisa.ac.za.

Abstract

Convolutional neural networks (CNNs) have been applied to visual tasks since the late 1980s. However, despite a few scattered applications, they were dormant until the mid-2000s when developments in computing power and the advent of large amounts of labeled data, supplemented by improved algorithms, contributed to their advancement and brought them to the forefront of a neural network renaissance that has seen rapid progression since 2012. In this review, which focuses on the application of CNNs to image classification tasks, we cover their development, from their predecessors up to recent state-of-the-art deep learning systems. Along the way, we analyze (1) their early successes, (2) their role in the deep learning renaissance, (3) selected symbolic works that have contributed to their recent popularity, and (4) several improvement attempts by reviewing contributions and challenges of over 300 publications. We also introduce some of their current trends and remaining challenges.

PMID:
28599112
DOI:
10.1162/NECO_a_00990

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